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library(dplyr) | |
library(rlang) | |
library(purrr) | |
x <- tibble(year=2011:2015, | |
value=runif(5)) | |
# extending the tibble class | |
class(x) <- unique(c("tbl_cf", class(x))) | |
attr(x, ".index") <- "year" |
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library(tidyverse) | |
library(tweenr) | |
library(magick) | |
m <- 2e2 | |
min_s <- 4 | |
k <- 2e2/(min_s) | |
img <- image_graph(600, 600, res = 96) | |
plot_snowflakes <- function(data){ |
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# Data collected from the following link: | |
# https://us.teamblind.com/article/Stop-asking-for-tech-company-offer-numbers-Read-this-and-thank-me-later-sN8uYpop | |
comp <- data.frame(stringsAsFactors=FALSE, | |
company = c("Google", "Google", "Google", "Google", "Google", "Google", | |
"Google", "Google", "Google", "Google", "Google", "Google", | |
"Google", "Google", "Google", "Google", "Google", "Google", | |
"Google", "Facebook", "Facebook", "Facebook", "Facebook", | |
"Facebook", "Facebook", "Facebook", "Facebook", "Facebook", | |
"Facebook", "Facebook", "Facebook", "Facebook", "Facebook", |
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set.seed(1) | |
tibble::tibble(year = seq(1900,1999) * | |
sample(c(1, NA), 100, replace = TRUE), | |
value=runif(100), | |
gap_id=inverse.rle(structure(list(lengths=rle(is.na(year))$lengths, | |
values=cumsum(rle(is.na(year))$values)* | |
rle(is.na(year))$values), class="rle"))) | |
#> # A tibble: 100 x 3 | |
#> year value gap_id | |
#> <dbl> <dbl> <int> |
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--- | |
title: "Presentation Ninja" | |
subtitle: "⚔<br/>with xaringan" | |
author: "Yihui Xie" | |
date: "2016/12/12 (updated: `r Sys.Date()`)" | |
output: | |
xaringan::moon_reader: | |
lib_dir: libs | |
css: | |
- default |
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# create geometry matrix | |
geom <- cbind(x=c(4,4,2,5,2,3,3,3,4,2), | |
y=c(6,2,3,4,5,2,4,7,8,0)) | |
# create data frame with special topology list column | |
df <- tibble::tibble(name="John", age=32, | |
topo=list( # list column containing polygon(s) and inner/outer flag | |
list(poly = list(c(10, 5, 8, 9, 4, 2), | |
c(3, 6, 1, 7)), | |
type = c(1L, 0L))) |
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library(readxl) | |
library(tidyverse) | |
library(hms) | |
library(hrbrthemes) | |
library(extrafont) | |
read_excel("lundaloppet2018.xlsx", col_names = F, col_types = "text") %>% | |
janitor::clean_names() %>% | |
mutate(x2=parse_number(x2), | |
x5n = parse_number(x5), |
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set.seed(42) # The answer to life, the universe and everything | |
## This is what the expert said | |
p10 <- 8 | |
p50 <- 12 | |
p90 <- 25 | |
# I can approximate it with Myerson distribution defined by quantiles | |
prior_predictive_elicited <- tidyear::rMyerson(1e5, p10, p50, p90, tl=0.2) |
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compute_hpdi_TJ <- function(xs, prob = .9) { | |
x_sorted <- sort(xs) | |
n <- length(xs) | |
num_to_keep <- ceiling(prob * n) | |
num_to_drop <- n - num_to_keep | |
possible_starts <- seq(1, num_to_drop + 1, by = 1) | |
# Just count down from the other end | |
possible_ends <- rev(seq(from = n, length = num_to_drop + 1, by = -1)) |
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library(tidyverse) | |
library(rvest) | |
robotstxt::paths_allowed("http://ukrcensus.gov.ua") | |
urlencode_utf_win1251 <- function(x){ | |
d <- "0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18, 19: 19, 20: 20, 21: 21, 22: 22, 23: 23, 24: 24, 25: 25, 26: 26, 27: 27, 28: 28, 29: 29, 30: 30, 31: 31, 32: 32, 33: 33, 34: 34, 35: 35, 36: 36, 37: 37, 38: 38, 39: 39, 40: 40, 41: 41, 42: 42, 43: 43, 44: 44, 45: 45, 46: 46, 47: 47, 48: 48, 49: 49, 50: 50, 51: 51, 52: 52, 53: 53, 54: 54, 55: 55, 56: 56, 57: 57, 58: 58, 59: 59, 60: 60, 61: 61, 62: 62, 63: 63, 64: 64, 65: 65, 66: 66, 67: 67, 68: 68, 69: 69, 70: 70, 71: 71, 72: 72, 73: 73, 74: 74, 75: 75, 76: 76, 77: 77, 78: 78, 79: 79, 80: 80, 81: 81, 82: 82, 83: 83, 84: 84, 85: 85, 86: 86, 87: 87, 88: 88, 89: 89, 90: 90, 91: 91, 92: 92, 93: 93, 94: 94, 95: 95, 96: 96, 97: 97, 98: 98, 99: 99, 100: 100, 101: 101, 102: 102, 103: 103, 104: 104, 105: 105, 106: 106, 107: 107, 108: 108, 109: 109, 110: 110, |
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